DETAILED ACTION
This action is in reply to the amendment filed on 11/21/2025.
Claim 3, 4, 14, 15 were previously canceled.
Claims 13 and 19 have been canceled.
Claims 1, 2, 5-12, 16-18 and 20-24 have been amended.
Claims 25-26 have been added.
Claims 1, 2, 5-12, 16-18 and 20-26 are pending and have been examined.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
With regard to the 101 rejection, the arguments have been considered but they are not persuasive. The applicant asserted that “[claims] 1, 11 and 18 are patent eligible based on at least these additional elements which are outside the alleged abstract ideas, and integrate the claims into a practical application under Prong Two Step 2A.” However, the limitations are not indicative of integration into a practical application: They are Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).
Similarly, under step 2B Prong Two, the Applicant asserted that “the additional elements noted above, particularly when viewed as an ordered combination, amount to ‘significantly more’ than any alleged ineligible subject matter . . .”. However, the limitations are not indicative of an inventive concept (aka “significantly more”): They are Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).
Therefore, the claim is not patent eligible under 35 U.S.C. 101.
With regard to the 103 rejection, the arguments have been considered but they are not persuasive. It has been noted that a substantial part of claim 5 has been incorporated into the independent claims. But, the applicant has not canceled claim 5. And furthermore, upon reconsideration, the cited reference Sehrer disclosed the amended limitations. It would have been obvious to one of ordinary skill in the art before the effective time of filing to modify Ranganathan in view of Milton by combining the feature of determining whether the transaction is a false positive using machine learning model as taught by Sehrer because modifying Ranganathan in view of Milton helps to reduce the false positive incidents in the system (abstract). Therefore, the combination is obvious.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 2, 5-12, 16-18 and 20-26 are directed to a system or method which are one of the statutory categories of invention. (Step 1: YES).
Claims 1, 2, 5-12, 16-18 and 20-26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 1, 2, 5-13, and 16-24 are directed to an abstract idea in the Methods of Organizing Human Activity grouping. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional computer elements, which are recited at a high level of generality, provide generic computer functions that do not add meaningful limits to practicing the abstract idea.
Claim 1, recites, in part, A computer-implemented method for determining location- based fraud alerts associated with a transaction, the method comprising: receiving transaction data associated with the transaction, wherein the transaction data includes a transaction time and a transaction location; determining a user associated with the transaction; determining, based at least in part on the transaction data, an indication of potential location-based fraud associated with the transaction; in response to determining the indication (a) determining a first computing device associated with the user; (b) initiating retrieval, via a network, of device location data from the first computing device; and (c) determining, by inputting at least a portion of the transaction data and the device location data into a trained machine learning program, a likelihood that the indication of the potential location-based fraud is false; and providing, via the network, to a second computing device associated with the transaction, and based at least in part on the likelihood: a first instruction causing the second computing device to complete execution of the transaction, or a second instruction preventing the second computing device from executing the transaction. The limitations are directed to concept of detecting location based fraudulent in transactions (sales behaviors-commercial interactions), which belongs in Certain Methods of Organizing Human Activity. Accordingly, the claim recites an abstract idea.
Claims 11 recites, in part, A computer system configured to prevent or complete transactions based on location-based fraud alerts associated with a transaction, the computer system comprising: one or more processors; and a memory storing instructions that, when executed by the one or more processors, cause the computer system to perform operations comprising: receiving transaction data associated with the transaction, wherein the transaction data includes a transaction time and a transaction location; determining a user associated with the transaction; determining, based at least in part on the transaction data an indication of potential location-based fraud associated with the transaction; in response to determining the indication: (a) determining a first computing device associated with the user (b) initiating retrieval, via a network, of device location data from the first computing device; and (c) determining, by inputting at least a portion of the transaction data and the device location data into a trained machine learning program, a likelihood that the indication of the potential location-based fraud is false; and providing, via the network, to a second computing device associated with the transaction, and based at least in part on the likelihood: a first instruction causing the second computing device to complete execution of the transaction, or a second instruction preventing the second computing device from executing the transaction. The limitations are directed to concept of detecting location based fraudulent in transactions (sales behaviors-commercial interactions), which belongs in Certain Methods of Organizing Human Activity. Accordingly, the claim recites an abstract idea.
Claim 18 recites, in part, A computer-implemented method for determining false positive location-based fraud alerts associated with a transaction, the method comprising: receiving transaction data associated with the transaction, wherein the transaction data includes a transaction time and a transaction location; determining a user associated with the transaction; determining, based at least in part on the transaction data, an indication of potential location-based fraud associated with the transaction; in response to determining the indication (a) determining a first computing device associated with the user; (b) initiating retrieval, via a network, of device location data from the first computing device; and (c) determining, by inputting at least a portion of the transaction data and the device location data into a trained machine learning program, a likelihood that the indication of the potential location-based fraud is false; and providing, via the network, to a second computing device associated with the transaction, and based at least in part on the likelihood: a first instruction causing the second computing device to complete executing the transaction, or a second instruction preventing the second computing device from executing the transaction. The limitations are directed to concept of detecting location based fraudulent in transactions (sales behaviors-commercial interactions), which belongs in Certain Methods of Organizing Human Activity. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements such as one or more processors, a memory, one communication interface, computer-executable instructions, one or more non-transitory computer-readable media storing computer-executable instructions, a user computing device, a wireless network, a vehicle-installed computing device, a rule engine, smart home controller, home-mounted sensor, a user device to perform transferring, receiving, determining. The generic computer components are recited at a high-level of such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Next the claim as a whole is analyzed to determine whether any element, or combination of elements, is sufficient to ensure the claim amounts to significantly more than an abstract idea. Claims 1, 11 and 18 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements of at least a computing device to perform receiving and identifying data are merely additional elements performing the abstract idea on a generic device i.e., abstract idea and apply it. MPEP 2106.05(f). There is no improvement to computer technology or computer functionality MPEP 2106.05(a) nor a particular machine MPEP 2106.05(b) nor a particular transformation MPEP 2106.05(c). Given the above reasons, a generic processing device associated with verifying a fraudulent transaction is not an Inventive Concept. Thus, the claim is not patent eligible.
The dependent claims have been given the full two part analysis (Step 2A – 2-prong tests and step 2B) including analyzing the additional limitations both individually and in combination. The Dependent claim(s) when analyzed both individually and in combination are also held to be patent ineligible under 35 U.S.C. 101 because for the same reasoning as above and the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea. The additional limitations of the dependent claim(s) when considered individually and as ordered combination do not amount to significantly more than the abstract idea.
Claims 2, 12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. The claim(s) recite(s) receiving potential fraud alert. This judicial exception is not integrated into a practical application because the limitations are Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). The claim(s) recite(s) additional element such as vehicle-installed computing device, a home-installed device, or a wearable computing devices which are not sufficient to amount to significantly more than the judicial exception because the limitations are adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).
Claim 5 is rejected under 35 U.S.C. 101 because the claimed invention is directed to judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. The claim(s) recite(s) receiving potential fraud alert. This judicial exception is not integrated into a practical application because the limitations are Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). The claim(s) does/do not include additional elements (such as machine learning program) that are sufficient to amount to significantly more than the judicial exception because the limitations are adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).
Claim 6, 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. The claim(s) recite(s) determining a time difference between time associated with the customer locations. This judicial exception is not integrated into a practical application because the limitations are Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitations are adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).
Claim 7 is rejected under 35 U.S.C. 101 because the claimed invention is directed to judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. The claim(s) recite(s) an abstract idea of receiving an IP address and determining the customer location. This judicial exception is not integrated into a practical application because the limitations are Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). The claim(s) does/do not include additional elements (i.e., IP address) that are sufficient to amount to significantly more than the judicial exception because the limitations are adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).
Claim 8 is rejected under 35 U.S.C. 101 because the claimed invention is directed to judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. The claim(s) recite(s) an abstract idea of receiving a first location and a second location with first time and second time This judicial exception is not integrated into a practical application because the limitations are adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). The claim(s) does/do not include additional elements (such as a computing device, a location) that are sufficient to amount to significantly more than the judicial exception because the limitations are adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).
Claims 9, 16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. The claim(s) recite(s) receiving occupancy data from at least smart home controller. This judicial exception is not integrated into a practical application because the limitations are Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). The claim(s) does/do not include additional elements (including a home-mounted sensor, a smart home controller, occupancy data, a customer, a home) that are sufficient to amount to significantly more than the judicial exception because the limitations are adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).
Claims 10, 17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. The claim(s) recite(s) an abstract idea receiving network connection data. This judicial exception is not integrated into a practical application because the limitations are Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). The claim(s) does/do not include additional elements (i.e., user computing device) that are sufficient to amount to significantly more than the judicial exception because the limitations are adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).
Claims 21 and 23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. The claim(s) recite(s) retrieving device usage data from each of a plurality of computing devices associated with the customer. This judicial exception is not integrated into a practical application because the limitations are Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). The claim(s) does/do not include additional elements (such as a user computing devices) that are sufficient to amount to significantly more than the judicial exception because the limitations are adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).
Claims 22 and 24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. The claim(s) recite(s) identifying a plurality of computing devices associated with the customer. This judicial exception is not integrated into a practical application because the limitations are Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). The claim(s) does/do not include additional elements (such as computing devices) that are sufficient to amount to significantly more than the judicial exception because the limitations are adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).
Claim 25 is rejected under 35 U.S.C. 101 because the claimed invention is directed to judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. The claim(s) recite(s) an online search for the product by the user. This judicial exception is not integrated into a practical application because the limitations are Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). The claim(s) does/do not include additional elements (such as computing devices) that are sufficient to amount to significantly more than the judicial exception because the limitations are adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).
Claim 26 is rejected under 35 U.S.C. 101 because the claimed invention is directed to judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. The claim(s) recite(s) an online search for the product by the user, a visit to a website associated with the product by the user, an online search for products related to the product, or an online search for the products in a price range corresponding to a transaction amount of the transaction. This judicial exception is not integrated into a practical application because the limitations are Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). The claim(s) does/do not include additional elements (such as computing devices) that are sufficient to amount to significantly more than the judicial exception because the limitations are adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).
Therefore, Claims 1, 2, 5-12, 16-18 and 20-26 are not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 6-8, 10-11, 14- 15, 17- 18, 20-24, 25 and 26 are rejected under 35 U.S.C. 103 as being unpatentable over Ranganathan (US 2012/0209773 A1) in view of Milton et al. (US 2015/0199699 A1) in further view of Sehrer (US 8,983,868 B1).
Claims 1, 11 and 18 are grouped together. Claim 11, for instant is disclosed: Ranganathan discloses: A computer system configured to prevent transmission of false positive location-based fraud alerts associated with a transaction, the computer system comprising: one or more processors; and a memory storing instructions that, when executed by the one or more processors, cause the computer system to perform operations comprising: receiving transaction data associated with the transaction, wherein the transaction data includes a transaction time and a transaction location; (Ranganathan, see at least par. [0023] “ When it has been determined that fraud is likely, either the user, the merchant/seller, and/or an issuer of the credit card can be alerted (block 208) . . .” & see at least par. [0028] “. . . When a user swipes a credit card at a store to pay for goods/services, the physical location of the merchant/service provider is sent the bank/financial institution/payment provider and onto a server in the fraud alerting system where the physical location of the merchant/service provider is compared against the last known location of the user's cell phone in real-time . . .”) Interpretation: the data includes both the location of the user and at any given time (or in real-time);
in response to determining the indication: determining a first computing device associated with the user (Ranganathan, see at least par. [0041] “ It is then determined if the transaction device is authorized for use with the credit card or payment instrument (block 306) . . .”); (b) initiating retrieval, via a network, of device location data from the]first computing device (Par. [0042]) “. . . The IP geolocation and the mobile device location are then compared to determine if they match within a predefined distance threshold (block 314) . . .”)
determining a customer associated with the transaction (par. [0020]) Interpretation: information of the user could include cell phone number; location data from the user computing device; (Ranganathan, Fig. 1 step 206 & 208, Par. [0022] “A likelihood of fraud in the payment transaction is then determined based on a difference between the location of the user and the location of the transaction (block 206). This difference may vary, depending on system fraud requirements, user settings, accuracy or resolution of the location determinations, etc. For example, if the distance between the user device and the transaction device is more than 500 meters, a possible fraud may be identified.” & [0023] “When it has been determined that fraud is likely, either the user, the merchant/seller, and/or an issuer of the credit card can be alerted (block 208).”) Location of the user is detected;
and providing, via the network, to a second computing device associated with the transaction (par. [0023] “ When it has been determined that fraud is likely, either the user, the merchant/seller, and/or an issuer of the credit card can be alerted (block 208). The alert may be through a text, email, or call to the user device, the merchant, and/or the credit card issuer . . .”), and based at least in part on the likelihood: a first instruction causing the second computing device to complete execution of the transaction, or a second instruction preventing the second computing device from executing the transaction (par. [0032] Interpretation: the transaction disclosed in this portion is based at least in part on the location and at the time of transaction (geo IP address) & par. [0042] Interpretation: If no fraud alert is detected, the transaction is approved, completed and no alert would be generated and sent to a mobile device.)
Ranganathan does not disclose the following; however, Milton teaches:
determining, based at least in part on the transaction data, an indication of potential location-based fraud associated with the transaction (Milton, [0030] “Some embodiments include a fraud detector 24 which may include an automated process run by a financial institution that detects anomalous behavior indicative of fraud based, in part, on correlations (or lack thereof) between financial transactions and patterns identified by the geolocation analytics platform 12. For instance, in some embodiments, the fraud detector 24 may submit a query to the geolocation analytics platform 12 based on a financial transaction, such as the purchase of a particular type of automobile, and the geolocation analytics platform 12 may respond with an audience classification of the user. In some embodiments the fraud detector 24 may determine whether the user who engaged in the financial transaction is likely to be a member of the audience for such purchases based on the data provided by the geolocation analytics platform 12. For example, a user who is not a member of an audience in Austin, Tex. that is present in Austin golf courses regularly, upon purchasing a set of golf clubs, may trigger a fraud alert, when the fraud detector receives a report for the geolocation analytics platform 12 that the user is not a member of an Austin, Tex., golf-playing audience.” & Par. [0059]) Interpretation: the system determines that a transaction could potentially fraudulent by using the geolocation analytics platform and the classification of the user. Machine learning is discussed in Par. [0059].
It would be obvious to one of ordinary skill in the art before the effective filing date to modify Ranganathan by combining the feature of using machine learning an inputting the transaction data into a rule engine of as taught by Milton, because modifying Ranganathan helps to better predict whether the transaction is actually a fraudulent activity by reducing a measure of error when applied to a training set of data (par. [0055]). Therefore, the combination is obvious.
Ranganathan in view of Milton does not disclose the following; however, Sehrer teaches:
and (c) determining, by inputting at least a portion of the transaction data and the device location data into a trained machine learning program (Sehrer, Col. 8, ln 5-19) Interpretation: the system uses the social (login) model to determine location of the user and such model could be a machine learned model, a likelihood that the indication of the potential location-based fraud is false (Sehrer, Col. 5 ln 21-40) Interpretation: the system performs certain determining steps to determine whether the fraud alert transaction is a false positive.
It would have been obvious to one of ordinary skill in the art before the effective time of filing to modify Ranganathan in view of Milton by combining the feature of determining whether the transaction is a false positive using machine learning model as taught by Sehrer because modifying Ranganathan in view of Milton helps to reduce the false positive incidents in the system (abstract). Therefore, the combination is obvious.
Claim 5 is disclosed: Ranganathan in view of Milton in further view of Sehrer teaches: The computer-implemented method of claim 1,further comprising: wherein the indication is determined by inputting at least a portion of the transaction data into a rules engine (Sehrer, Col. 8, ln 5-19) Interpretation: the system uses the social (login) model to determine location of the user, portion data, and such model could be rule engine model.
It would be obvious to one of ordinary skill in the art before the effective filing date to modify Ranganathan in view of Milton by combining the feature of using machine learning an inputting the transaction data into a rule engine of as taught by Sehrer, because modifying Ranganathan in view of Milton helps to better predict whether the transaction is actually a fraudulent activity by reducing a measure of error when applied to a training set of data. Therefore, the combination is obvious.
Claim 6, 20 are grouped together. Claim 6 is disclosed, for instance: Ranganathan in view of Milton in further view of Sehrer teaches: The computer-implemented method of claim 1. Ranganathan teaches: further comprising: determining a time difference between the time associated with the customer location data and the transaction time, and preventing the transaction from being executed based on determining the time difference is within a time duration threshold (Ranganathan, par. [0029]) Interpretation: the time duration threshold, under BRI, corresponds to confidence level.
Claim 7 is disclosed: Ranganathan in view of Milton in further view of Sehrer teaches: The computer-implemented method of claim 1. Ranganathan further teaches: wherein initiating retrieval of the device location data further comprises: receiving an IP address associated with a computing device used to complete the transaction(Ranganathan, Par. [0042]) Interpretation: IP address and geolocation are collected by the system.
Claim 8 is disclosed: Ranganathan in view of Milton in further view of Sehrer teaches: The computer-implemented method of claim 1. Ranganathan further teaches: wherein initiating retrieval of the device location data further comprises: receiving, from a first computing device associated with the customer, a first location associated with a first time (see at least par. [0049] “. . . The illustration builds on the specific case where the person was last spotted within a few miles of San Francisco International Airport (SFO). It considers time of day, departing flights around that time, average commercial flight speed, FAA flight records for public and private flights and potential destinations . . .”) Interpretation: first time could correspond to departing time; and receiving, from a second computing device associated with the customer, a second location associated with a second time, wherein determining the customer location comprises matching the transaction time to a nearest time of the first time and the second time. (Ranganathan, par. [0050] “The last known locations are shown in positions 1 and 2. The time, location, heading and speed are known. At positions 3, 4, and 5, the cell phone location is unknown (perhaps it ran out of batteries). Based on the information collected at positions 1 and 2, a trajectory along the route can be calculated to predict the user's position at a given time at positions 3,4, and 5 . . .” & [0051]) Interpretation: The cited portion discloses determining second time matching second location, and therefore, the system would be able to verify transaction time
Claims 10, 17 are grouped together. Claim 10, for instance, is disclosed. Ranganathan in view of Milton discloses claim 1. Ranganathan further teaches: wherein initiating retrieval of the device location data further comprises at least one of: receiving network connection data associated with the user computing device; receiving Internet browsing data associated with the user computing device; or receiving social media activity data associated with the user computing device. (Ranganathan, [0026] “Knowing the location of a user associated with the payment account or the cardholder and comparing it to the location of the transaction, both in the physical world (location of the point of sale terminal) or in an online world (geo IP location of the computer/device used in the transaction)”.) Online data search as geo IP location is collected by the system.
Claims 21 and 23 are grouped together. Claim 21, for instance, is disclosed. Ranganathan in view of Milton discloses The computer-implemented method of claim 1. Ranganathan further teaches: wherein determining the user computing device comprises: retrieving device usage data from each of a plurality of computing devices associated with the customer (Ranganathan, see at least par. [0062] “. . . User accounts may also contain information about one or more devices associated with the user and the user account, such as a device ID, IP address, current location of device, history of device locations, and other information about device location as described herein, such as direction of movement.”); and determining, based at least in part on the retrieved device usage data, a likelihood that the user computing device was present with the customer at the time corresponding to the transaction time (par. [0042]) Interpretation: the location of the user device is retrieved to compare with the customer’s location at the transaction time.
Claims 22 and 24 are grouped together. Claim 21, for instance, is disclosed. Ranganathan in view of Milton discloses claim 1. Milton, however, teaches: wherein determining the user computing device comprises: identifying a plurality of computing devices associated with the customer (see at least par. [0056] “. . . ] FIG. 2 shows an example of a process 70 for learning an audience member function based on training data. In some cases, the model includes obtaining a training set of geographic data describing geolocation histories of a plurality of mobile devices, as indicated by block 72 . . .”) the mobile devices are identified as part of the training data; retrieving device location data from each of the plurality of computing devices (Milton, see at least par. [0026] “. . . In some cases, the geographic locations sensed by the user devices 18 may be reported to the content server 22 for selecting content based on location to be shown on the mobile devices 18, and in some cases, location histories (e.g., a sequence of timestamps and geographic location coordinates) are acquired by the geographic-data providers 20 . . .”) The locations of the user devices are retrieved; and identifying the user computing device as a particular one of the plurality of computing devices, based at least in part on the device location data retrieved from the plurality of computing devices (Milton, see at least par. [0048] “. . . In some cases, an audience membership vector may be calculated based on a given geographic location, a given user identifier (e.g., a device identifier), and given time, with each component of the vector indicating membership in a corresponding audience . . .”) Interpretation: a computing device is located from a vector of devices.
It would be obvious to one of ordinary skill in the art before the effective filing date to modify Ranganathan in view of Milton by combining the features of identifying plurality of devices as taught by Milton, because modifying Ranganathan in view of Milton helps to better predict whether the transaction is actually a fraudulent activity by reducing a measure of error when applied to a training set of data. Therefore, the combination is obvious.
Claim 25, for instance, is disclosed. Ranganathan in view of Milton discloses The computer-implemented method of claim 1. Ranganathan further teaches: The computer-implemented method of claim 21, wherein the information indicative of the interest of the user related to the product includes at least one of: an online search for the product by the user, a visit to a website associated with the product by the user, an online search for products related to the product, or an online search for the products in a price range corresponding to a transaction amount of the transaction (Ranganathan, see at least par. [0059] “. . . Merchant server 640 includes a database 645 identifying available products and/or services (e.g., collectively referred to as items) which may be made available for viewing and purchase by user 605. Merchant server 640 also includes a marketplace application 650 which may be configured to serve information over network 660 to browser 615 of user device 610. In one embodiment, user 605 may interact, either through user device 610 or transaction device 635, with marketplace application 650 through browser applications over network 660 in order to view various products, food items, or services identified in database 645 . . .”).
Claim 26, for instance, is disclosed. Ranganathan in view of Milton discloses The computer-implemented method of claim 1. Ranganathan further teaches: The computer system of claim 23, wherein the information indicative of the interest of the user related to the product includes at least one of: an online search for the product by the user, a visit to a website associated with the product by the user, or an online search for products related to the product (Ranganathan, see at least par. [0059] “. . . Merchant server 640 includes a database 645 identifying available products and/or services (e.g., collectively referred to as items) which may be made available for viewing and purchase by user 605. Merchant server 640 also includes a marketplace application 650 which may be configured to serve information over network 660 to browser 615 of user device 610. In one embodiment, user 605 may interact, either through user device 610 or transaction device 635, with marketplace application 650 through browser applications over network 660 in order to view various products, food items, or services identified in database 645 . . .”).
Claims 2, 12 is rejected under 35 U.S.C. 103 as being unpatentable over Ranganathan (Fraud Alerting Using Mobile Phone Location – US 2012/0209773 A1) in view of Milton et al. (US 2015/0199699 A1) in further view of Sehrer (US 8,983,868 B1) in further view of Gao et al. (US 2016/0210857 A1).
Claims 2, 12 are grouped together. Ranganathan in view of Milton in further view of Sehrer teaches claim 1. However, Gao teaches: The computer-implemented method of claim 1, wherein the user computing device comprises at least one of: a vehicle-installed computing device; a home-installed computing device; or a wearable computing device (see at least par. [0004] “. . . In some embodiments, the travel reporter system includes an electronic data sensor mounted on a vehicle and configured to obtain real-time data about an object located within a predetermined distance of the vehicle . . .” ).
It would be obvious to one of ordinary skill in the art before the effective filing date to modify Ranganathan by combining the feature of collecting data from one or more vehicle controller or mounter sensors and GPS data as taught by Gao, because modifying Ranganathan in view of Milton in further view of Sehrer helps to track the location of the user relative to the transaction’s location. Therefore, the combination is obvious.
Claims 9, 16 are rejected under 35 U.S.C. 103 as being unpatentable over Ranganathan (Fraud Alerting Using Mobile Phone Location – US 2012/0209773 A1) in view of Milton et al. (US 2015/0199699 A1) in further view of Sehrer (US 8,983,868 B1) in further view of Fadell et al. (US 2014/0266669 A1).
Claims 9, 16 are grouped together. Claim 9, for instance, is disclosed by Ranganathan in view of Milton in further view of Sehrer. However, Fadell teaches: wherein the obtaining the customer data comprises: receiving the customer data from at least one of a smart home controller or a home-mounted sensor associated with the customer, the acts further comprising: determining, based at least in part on the customer data, at least one of whether a home of the customer is presently occupied or vacant or how long the home of the customer has been vacant (Fadell, [0089] “According to embodiments, the occupants of the home can pre-program the smart-home environment 100 to broadcast specific alarms in response to specific detected conditions. For example, in the event of smoke detection, the smart-home environment 100 can broadcast via the network-connected smart devices and/or the home appliances, such as television and stereos, a pre-recorded message from the occupant notifying the occupants of a possible fire and providing emergency exit instructions. In another example, in case of detected home invasion, the smart-home environment 100 can broadcast a message to the intruders, notifying them that their presence has been detected, that the occupants possess and are trained to use firearms to protect their home, that the police have been notified, etc.”) Intrusion corresponds to the home is presently occupied.
It would be obvious to one of ordinary skill in the art before effective filing date to modify Ranganathan in view of Milton in further view of Sehrer by combining the feature of collecting data from smart home as taught by Fadell, because modifying Ranganathan in view of Milton in further view of Sehrer helps to determine whether information regarding the customer is stolen. Therefore, the combination is obvious.
Conclusion
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/TOAN DUC BUI/ Examiner, Art Unit 3693
/ELIZABETH H ROSEN/Primary Examiner, Art Unit 3693